Evolution of airports from a network perspective——An analytical concept

Analyzing airports' role in global air transportation and monitoring their development over time provides an additional perspective on the dynamics of network evolution. In order to understand the different roles airports can play in the network an integrated and multi-dimensional approach is needed. Therefore, an approach to airport classification through hierarchi-cal clustering considering several parameters from network theory is presented in this paper. By applying a 29 year record of global flight data and calculating the conditional transition probabil-ities the results are displayed as an evolution graph similar to a discrete-time Markov chain. With this analytical concept the meaning of airports is analyzed from a network perspective and a new airport taxonomy is established. The presented methodology allows tracking the development of airports from certain categories into others over time. Results show that airports of equal classes run through similar stages of development with a limited number of alternatives, indicating clear evolutionary patterns. Apart from giving an overview of the results the paper illustrates the exact data-driven approach and suggests an evaluation scheme. The methodology can help the public and industry sector to make informed strategy decisions when it comes to air transportation infras-tructure.     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

计算机网络安全及安全审计技术研究

通过对计算机安全和安全审计问题的简要分析，并结合网络系统的特点，力图较系统深入地探讨计算机网络安全及其审计技术；在此基础上，提出了一个网络安全审计模型。     

WIOBSS: The Chinese low-power digital ionosonde for ionospheric backscattering detection

The ionospheric laboratory of Wuhan University has developed an ionosonde over about seven years. The prototype is designed as a portable, low-power digital MF (medium frequency) and HF (high frequency) radio system with high range and Doppler resolution. The first system is used for ionospheric oblique backscattering detection and called WIOBSS (Wuhan Ionospheric Oblique Backscattering Sounding System). WIOBSS is a portable monostatic ionosonde for both ionospheric research and HF channel management, adopting alternate transmitting and receiving patterns to transmit long coded pulse trains for high pulse compression gain. The almost perfect sequences are applied to modulate the pulse train for sounding without range sidelobes and the echo phase is used to estimate the Doppler velocity and velocity fluctuation. This paper introduces the new techniques adopted by WIOBSS and presents some observations.     

单次特快波形信号的实时传输技术

论述一种单次特快波形信号的实时传输技术，介绍弹载遥测发射设备的工作概况，该设备从射频频率的角度看，它是一种调频体制的，从射频功率的角度看，它是一种脉冲体制的。该设备不但能传输特快波形信号，而且能传输缓变的常规遥测信号，设计方案新颖，是一种多用途的弹上遥测发射设备。     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

Experimental and numerical investigation on surface quality for two-point incremental sheet forming with interpolator

The unsatisfied surface quality seriously impedes the wide application of incremental sheet forming (ISF) in industrial field. As a novel approach, the interpolator method is a promising strategy to enhance the surface quality in ISF. However, the mechanism for the improvement of surface quality and the influence of interpolator properties on surface roughness are not well understood. In this paper, the influences of process variables (i.e. tool diameter, step size and thickness of interpolators) on the forming process (e.g. surface roughness, forming force and geometric error) are investigated through a systematic experimental approach of central composite design (CCD) in two-point incremental sheet forming (TPIF). It is obtained that the increase in thickness of interpolators decreases the surface roughness in direction vertical to the tool path while increases the surface roughness in direction horizontal to the tool path. Nevertheless, the combined influence between thickness of interpolators and process parameters (tool diameter and step size) is limited. Meanwhile, the placement of interpolator has little influence on the effective forming force of blank. In addition, the geometric error enlarges with the increase of step size and thickness of interpolator while decreases firstly and then increase with an increase in tool diameter. Finally, the influencing mechanism of the interpolator method on surface quality can be attributed to the decrease of the contact pressure due to the increase of contact area with the unchanged contact force. Meanwhile, the interpolator method eliminates the sliding friction on the surface of blank due to the stable relative position between the blank and the interpolator.     

Feasibility evaluation of failure models for predicting forming limit of metal foils

Forming limit of metal foil is an important index to evaluate its formability, and is of considerable significance to improve the quality of products. The ductile fracture (DF) behavior in microscale plastic deformation is remarkably affected by the geometry and grain size. To explore the size-dependent forming limit curve (FLC), the Holmberg and Marciniak tests of SUS304 foils with the thicknesses of less than 0.1 mm and diverse grain sizes were performed. In addition, the validity and feasibility of three types of existing failure models including Swift/Hill, Marciniak-Kuczynski (M-K) and DF criteria for predicting the micro-scaled FLCs were discussed. It is found that the Swift/Hill model possesses the worst accuracy with predicting deviation above 50%. Four classical DF criteria including Freudenthal, Ayada, Brozzo and Oh show great difference, and Oh model considering plastic anisotropy presents the best precision. The predicted deviation of M-K model is aggravated with increasing grain size and decreasing foil thickness, which is attributed to the intensified free surface roughening and transformation of fracture mechanism with miniaturization. This research thus provides a deeper understanding and valuable reference for the widespread application of FLC in microforming.     

Collaborative image compression and classification with multi-task learning for visual Internet of Things

Widespread deployment of the Internet of Things(Io T) has changed the way that network services are developed, deployed, and operated. Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T. Typically, the sender would compress images, and then through the communication network, the receiver would decode images, and then analyze the images for applications. However, image compression and semantic inference are generally conducted separately, and t...     

网络分析仪法测量稠油介电常数

用网络分析仪在Ｘ波段８～１１．５ＧＨｚ的６个频率点上,对大港、胜利、辽河油田稠油的介电常数进行测量,测量结果可以为微波采油工艺中的井下轴射器的设计及微波能利用率的估算提供理论依据,并对测量结果进行了误差分析。